Green Campus Assessment Supported by Remote Sensing Big Data: A Review of Concepts, Tools, Evidence Pathways, and Future Directions

Campus sustainability assessments increasingly rely on broad indicator systems, yet their empirical foundation remains heavily dependent on institutional self-reports, administrative records, and checklist-based scoring. These data types capture governance and educational activities effectively. However, they are less suitable for evaluating spatially heterogeneous or temporally dynamic environmental conditions, such as greenspace distribution, land use composition, and thermal environments. Such conditions are difficult to verify through self-reporting alone. Remote sensing and GIS offer potential solutions by providing externally verifiable and cross-campus comparable measurements, but their integration into formal assessment standards remains underdeveloped. The literature reveals three persistent gaps: an evidence gap (over-reliance on self-reports), a spatial gap (limited inclusion of spatially explicit indicators), and a standardization gap (case studies without transferable protocols). These gaps are relevant in China, where diverse campus forms intensify comparability challenges. To address these issues within the Chinese context, this review proposes a conceptual two-level framework. The framework comprises a comprehensive assessment structure covering governance, education, operations, environment, and participation. It is paired with a separate spatial evidence module based on remote sensing and GIS. This framework is a conceptual reference and an operational pathway, rather than an empirically validated or calibrated tool. The framework intends to guide the systematic integration of spatial evidence into future Chinese green campus standards. However, threshold values, weighting schemes, and contextual adjustment factors would require further empirical testing and policy deliberation.